Quality control and filtering results from cellranger

Sample info and environment setup

PRJNA723584

setwd("/media/jacopo/Elements/re_align/MM/PRJNA723584/SAMN18822749/SRR14295362/")
# Load the libraries (from Sarah script + biomart)
library(tidyverse) # packages for data wrangling, visualization etc
library(Seurat) # scRNA-Seq analysis package
library(clustree) # plot of clustering tree 
library(ggsignif) # Enrich your 'ggplots' with group-wise comparisons
library(clusterProfiler) #The package implements methods to analyze and visualize functional profiles of gene and gene clusters.
library(org.Hs.eg.db) # Human annotation package neede for clusterProfiler
library(ggrepel) # extra geoms for ggplo2
library(patchwork) #multiplots
library(reticulate)

Load and process cellranger data

Load and do the QC for the cellranger data

#list.files(".")
dat <- Read10X(data.dir ="./out/counts_filtered/")
dat <- CreateSeuratObject(dat) # Create the seurat object from the 10x data
kb.initial <- dat@assays[["RNA"]]@counts@Dim[[2]]
cat("Initial number of cells:", kb.initial, 
    "\nNumber of genes:",  dat@assays[["RNA"]]@counts@Dim[[1]])
## Initial number of cells: 9940 
## Number of genes: 36601

Quality Control

Empty cells were already filtered, check for % mt RNA and death markers:

# first calculate the mitochondrial percentage for each cell
dat$percent_mt <- PercentageFeatureSet(dat, pattern="^MT.")
# make violin plots
mt_rna = 10
max_counts = 40000



# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
# Check some feature-feature relationships
# % mt RNA vs n Counts, n Features vs n Counts
VlnPlot(dat, features = c("nCount_RNA", "nFeature_RNA", "percent_mt"))  + geom_hline(yintercept=mt_rna, linetype = "dotted")

plot1 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "percent_mt")
plot1 <- plot1 + geom_hline(yintercept=mt_rna, linetype = "dotted")
plot2 <- FeatureScatter(dat, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot2 <- plot2 + geom_vline(xintercept = max_counts, linetype = "dotted")
plot1 

plot2

##  cells retained by mt RNA content ( 10 %): 9369 
##  percentage of retained cells: 94.26 %
## cells retained by counts ( 40000 ): 9336 
##  percentage of retained cells: 93.92 %

Check the distribution of the cells with low counts and control death markers:

min_counts = 400


hist(dat@meta.data$nCount_RNA, breaks = 100, xlab = "Counts")

hist(dat@meta.data$nCount_RNA, breaks = 1000, xlab = "Counts", xlim = c(0,5000))

hist(dat@meta.data$nCount_RNA, breaks = 10000, xlab = "Counts", xlim = c(0,1000))
abline(v=min_counts, col="red", lty = 3)

The evident peak of cells with < 200 counts could contain dying cells.

# Subset the dataset to focus only on those cells with low counts
dat.lowcount <- subset(dat, subset = nCount_RNA < min_counts)

# Get the mean of the counts for each gene and sort them decreasing
meanCounts <- rowMeans(GetAssayData(object = dat.lowcount, slot = 'counts'))
meanCounts <- sort(meanCounts, decreasing = T)

# A boxplot can help to observe the distribution of the means
#boxplot(meanCounts)

# Print the most highly expressed genes
head(meanCounts, 30)
##    MT-CO2       VIM     GAPDH     IGLC2    MT-CO1    EEF1A1    MT-CO3   MT-ATP6 
## 2.1280938 1.6695614 1.5931394 1.5149805 1.4554928 1.3000434 1.2570560 1.2336083 
##    MALAT1     RPL10      FTH1     RPLP1     RPS18    TMSB10       B2M     IGHA1 
## 1.2227529 1.1610942 1.1458967 1.0030395 0.9748155 0.9531046 0.9209726 0.9079462 
##    S100A6      RPS3     RPS12     RPL13     RPL41     RPS4X    MT-CYB      MT2A 
## 0.9009987 0.8962223 0.8818932 0.8814590 0.8792879 0.8675640 0.8028658 0.7954842 
##     RPL19    TMSB4X     RPS24     RPS23       FTL     RPL39 
## 0.7815892 0.7611811 0.7481546 0.7320886 0.7047330 0.6925749
## cells retained by counts ( 400 ): 7032 
##  percentage of retained cells: 70.74 %

dir.create("result")
saveRDS(dat, file = "./result/SAMN18822749_clean_QC.Rds")

Feature selection

#Normalize
dat <- NormalizeData(dat)
# Find the first 4000 variabe features
dat <- FindVariableFeatures(dat, selection.method = "vst", nfeatures = 4000)

Data scaling

Set mean expression to 0 and variance across 1 to avoid highly expressed genes drive the forwarding analyses. Since negative expression is meaningless, scaled data are useful only for UMAP and clustering

# scale data, the scaled data are saved in:
# dat[["RNA"]]@scale.data

all.genes <- rownames(dat)

dat <- ScaleData(dat, vars.to.regress = c("percent_mt","nCount_RNA"))

Dimensionality reduction

dat <- RunPCA(dat, features = VariableFeatures(object = dat), verbose = F, seed.use = 1)
print(dat[["pca"]], dims = 1:5, nfeatures = 5)
## PC_ 1 
## Positive:  IGHA1, HERPUD1, IGLV3-21, KRT8, LAMP5 
## Negative:  TYMS, STMN1, HMGB2, TK1, TUBA1B 
## PC_ 2 
## Positive:  MTDH, LY6E, AZGP1, FKBP11, HSPD1 
## Negative:  CD74, UCP2, TMSB4X, RPS4Y1, ARHGDIB 
## PC_ 3 
## Positive:  TMSB4X, COTL1, CD52, PTPRC, ITGB2 
## Negative:  IGHA1, MZB1, HBD, HBB, AHSP 
## PC_ 4 
## Positive:  TFF3, MYH7, LINC00665, MYH6, CD9 
## Negative:  LINC01287, IFI27, XAF1, PCAT19, PCDH20 
## PC_ 5 
## Positive:  AHSP, HBA1, CA1, HBA2, HBB 
## Negative:  HSPA5, PDIA6, SDF2L1, LAMP5, HGF

UMAP

UMAP is a graph-based method of clustering. The first step in this process is to construct a KNN graph based on the euclidean distance in PCA space:

dat <- FindNeighbors(dat, dims = 1:20)

The graph now can be used as input for the function runUMAP()

dat <- RunUMAP(dat, dims = 1:20, seed.use = 1)
DimPlot(dat, reduction = 'umap', seed = 1)

Final plots:

## QC metrics

## markers